The power of deeper networks for expressing natural functions

16 May 2017

Papers citing "The power of deeper networks for expressing natural functions"

23 / 23 papers shown

Title
System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Rebecca G. Hart Omkar Sudhir Patil Zachary I. Bell Warren E. Dixon 7 0 0 15 May 2025
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss T. Getu Georges Kaddoum M. Bennis 40 1 0 13 Sep 2023
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold Yanhong Fei Xian Wei Yingjie Liu Zhengyu Li Mingsong Chen 28 6 0 16 Feb 2023
MOSAIC, acomparison framework for machine learning models Mattéo Papin Yann Beaujeault-Taudiere F. Magniette VLM 16 0 0 30 Jan 2023
When Expressivity Meets Trainability: Fewer than $n$ Neurons Can Work Jiawei Zhang Yushun Zhang Mingyi Hong Ruoyu Sun Zhi-Quan Luo 26 10 0 21 Oct 2022
Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation Jeffmin Lin Gil Goldshlager Lin Lin 35 22 0 07 Dec 2021
On the approximation of functions by tanh neural networks Tim De Ryck S. Lanthaler Siddhartha Mishra 21 137 0 18 Apr 2021
Augmenting Deep Classifiers with Polynomial Neural Networks Grigorios G. Chrysos Markos Georgopoulos Jiankang Deng Jean Kossaifi Yannis Panagakis Anima Anandkumar 19 18 0 16 Apr 2021
Deep ReLU Networks Preserve Expected Length Boris Hanin Ryan Jeong David Rolnick 26 14 0 21 Feb 2021
Expressivity of Deep Neural Networks Ingo Gühring Mones Raslan Gitta Kutyniok 16 50 0 09 Jul 2020
Interpreting and Disentangling Feature Components of Various Complexity from DNNs Jie Ren Mingjie Li Zexu Liu Quanshi Zhang CoGe 11 18 0 29 Jun 2020
Deep Residual Mixture Models Perttu Hämäläinen Martin Trapp Tuure Saloheimo Arno Solin 28 8 0 22 Jun 2020
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens BDL 41 172 0 23 Apr 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 38 300 0 08 Jan 2020
Optimal Function Approximation with Relu Neural Networks Bo Liu Yi Liang 25 33 0 09 Sep 2019
A Review on Deep Learning in Medical Image Reconstruction Hai-Miao Zhang Bin Dong MedIm 32 122 0 23 Jun 2019
Deep Network Approximation Characterized by Number of Neurons Zuowei Shen Haizhao Yang Shijun Zhang 20 182 0 13 Jun 2019
A Selective Overview of Deep Learning Jianqing Fan Cong Ma Yiqiao Zhong BDL VLM 28 136 0 10 Apr 2019
Nonlinear Approximation via Compositions Zuowei Shen Haizhao Yang Shijun Zhang 18 92 0 26 Feb 2019
On a Sparse Shortcut Topology of Artificial Neural Networks Fenglei Fan Dayang Wang Hengtao Guo Qikui Zhu Pingkun Yan Ge Wang Hengyong Yu 38 21 0 22 Nov 2018
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity Chulhee Yun S. Sra Ali Jadbabaie 18 117 0 17 Oct 2018
ResNet with one-neuron hidden layers is a Universal Approximator Hongzhou Lin Stefanie Jegelka 36 227 0 28 Jun 2018
Benefits of depth in neural networks Matus Telgarsky 148 602 0 14 Feb 2016